Facilitating personalized vehicle occupant comfort

ABSTRACT

A vehicle occupant comfort system, comprises a processor that stores computer executable components stored in memory. A plurality of sensors sense ambient conditions associated with exterior and interior conditions of a vehicle. A context component infers or determines context of an occupant of the vehicle. A comfort model component implicitly and explicitly trained on occupant comfort related data analyzes information from the plurality of sensors and context component. A comfort controller adjusts environmental conditions of a passenger compartment of the vehicle based at least in part on output of the comfort model component.

BACKGROUND

The subject disclosure relates generally to machine learning systems andin particular to utilizing machine learning systems to regulateenvironmental conditions within a vehicle to improve occupant comfort.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments of the invention. This summary is not intended toidentify key or critical elements, or delineate any scope of theparticular embodiments or any scope of the claims. Its sole purpose isto present concepts in a simplified form as a prelude to the moredetailed description that is presented later. In one or more embodimentsdescribed herein, systems, computer-implemented methods, apparatusand/or computer program products that facilitate performing root causeanalysis in dynamic software testing via probabilistic modeling aredescribed.

According to an embodiment, a system, comprises: a memory storing one ormore computer readable and executable components; and a processoroperably coupled to the memory and that executes the computer readableand executable components stored in the memory. The processor can beoperably coupled to: a plurality of sensors that sense ambientconditions associated with exterior and interior conditions of avehicle; and a context component that determines context of an occupantof one or more occupants in the vehicle. The processor can also beoperably coupled to a comfort model component that analyzes informationfrom the plurality of sensors and the context component; and a comfortcontroller that adjusts environmental conditions of a passengercompartment of the vehicle based on an output from the comfort modelcomponent.

In another embodiment, a computer-implemented method is provided. Thecomputer-implemented method can comprise sensing, using one or moresensors, ambient conditions associated with one or more conditions of avehicle; and determining, by a device operatively coupled to aprocessor, context of an occupant of the vehicle. Thecomputer-implemented method can also comprise analyzing, by the device,one or more ambient conditions and the context; and adjusting one ormore environmental conditions of a passenger compartment of the vehiclebased on the analyzing the one or more ambient conditions and thecontext.

In another embodiment, a computer program product for facilitatingvehicle occupant comfort is provided. The computer program product cancomprise a computer readable storage medium having program instructionsembodied therewith. The program instructions can be executable by aprocessor to cause the processor to: sense, using sensors, one or moreambient conditions associated with interior conditions of a vehicle; anddetermine, by the processor, a context of one or more occupants of thevehicle. The program instructions can also be executable to analyze, bythe processor, the one or more ambient conditions and the context; andadjust, by the processor, one or more conditions associated with thevehicle based on the one or more ambient conditions and the context.

In some embodiments, elements of one or more computer-implementedmethods can be embodied in different (or a combination of) forms. Forexample, one or more elements of a computer-implemented method may beembodied (without limitation) as (or with) a system, a computer programproduct, and/or another form.

DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an example, non-limiting systemthat utilizes machine learning systems to regulate environmentalconditions within a vehicle to improve driving comfort in accordancewith one or more embodiments described herein.

FIG. 2 illustrates a block diagram of an example, non-limiting systemthat utilizes machine learning systems to regulate environmentalconditions within a vehicle to improve driving comfort in accordancewith one or more embodiments described herein.

FIG. 3 illustrates a block diagram of an example, non-limiting systemthat utilizes machine learning systems, including a pattern recognitioncomponent, to regulate environmental conditions within a vehicle toimprove driving comfort in accordance with one or more embodimentsdescribed herein.

FIG. 4 illustrates a schematic block diagram of an example vehicle thatutilizes machine learning systems to regulate environmental conditionswithin to improve driving comfort in accordance with one or moreembodiments described herein.

FIG. 5 illustrates example non-limiting body and contextual parametersthat influence driving comfort in accordance with one or moreembodiments described herein.

FIG. 6 illustrates example non-limiting body sensor and contextual datathat influence vehicle occupant comfort in accordance with one or moreembodiments described herein.

FIG. 7 illustrates an example of training a comfort model and utilizingsuch trained model to adjust vehicle comfort facilities in accordancewith one or more embodiments described herein.

FIG. 8 illustrates an example system architecture that implements one ormore embodiments described herein.

FIG. 9 illustrates an example of inferring driving comfort index basedat least in part on body and contextual sensor data in accordance withone or more embodiments described herein.

FIG. 10 illustrates example construction of training samples inaccordance with one or more embodiments described herein.

FIG. 11 illustrates an example calculation of comfort index for negativesample(s) in accordance with one or more embodiments described herein.

FIG. 12 illustrates an example of determining damaging drivingenvironmental parameters in accordance with one or more embodimentsdescribed herein.

FIG. 13 illustrates an example flow diagram of regulating vehicleenvironment to facilitate occupant comfort in accordance with one ormore embodiments described herein.

FIG. 14 illustrates a block diagram of an example, non-limitingoperating environment in which one or more embodiments described hereincan be facilitated.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is notintended to limit embodiments and/or application or uses of embodiments.Furthermore, there is no intention to be bound by any expressed orimplied information presented in the preceding Background or Summarysections, or in the Detailed Description section.

One or more embodiments are now described with reference to thedrawings, wherein like referenced numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea more thorough understanding of the one or more embodiments. It isevident, however, in various cases, that the one or more embodiments canbe practiced without these specific details.

One or more embodiments of the subject disclosure describes utilizingmachine learning systems to regulate environmental conditions within avehicle to improve driving comfort in accordance with one or moreembodiments described herein.

The number of drivers and associated driving time keep increasingrapidly year by year. Driving is an important activity in daily life.Research shows that driving comfort might be spoiled due to variousreasons, including foul air, vibration and noise, stress, fatigue,uncomfortable heat and humidity, narrow space and fixed position, etc.Embodiments described and claimed herein utilize machine learningsystems that have been explicitly or implicitly trained to learn,determine or infer occupant comfort and dynamically regulateenvironmental conditions of the vehicle to facilitate achieving occupantcomfort. For example, and as will be described in greater detail below,historical occupant comfort levels, occupant preferences, occupantcontext, ambient conditions inside and outside of the vehicle, vehicleor occupant state conditions, vehicle zones, multiple occupantpreferences and comfort, occupant zones and/or intersection areas ofoccupant zones are some of the many factors that can be taken intoconsideration by the machine learning system in connection withregulating vehicle environmental conditions to achieve occupant comfort.

The subject disclosure is directed to computer processing systems,computer-implemented methods, apparatus and/or computer program productsthat facilitate efficiently and automatically (e.g., without directhuman involvement) regulating vehicle environmental conditions utilizingmachine learning to help achieve occupant comfort. Humans are alsounable to perform the embodiments described here as they include, andare not limited to, performing, e.g., complex Markov processes, Bayesiananalysis, or other artificial intelligence based techniques based onprobabilistic analyses and evaluating electronic information indicativeof occupant comfort, determining whether countless multitudes ofprobability values assigned to occupant comfort exceed or fall belowvarious probability values.

The computer processing systems, computer-implemented methods, apparatusand/or computer program products employ hardware and/or software tosolve problems that are highly technical in nature. For example,problems are related to automated processing, determining or inferringoccupant comfort. These problems are not abstract and cannot beperformed as a set of mental acts by a human. For example, a human, oreven thousands of humans, cannot efficiently, accurately and effectivelymanually apply countless thousands of occupant comfort variables toinput points and perform analysis to determine that a probability valueassigned to a occupant comfort level exceeds a defined probabilityvalue.

In order to provide for or aid in the numerous inferences describedherein (e.g., inferring occupant comfort), components described hereincan examine the entirety or a subset of data to which it is grantedaccess and can provide for reasoning about or inferring states of asystem, environment, etc. from a set of observations as captured viaevents and/or data. Inference can be employed to identify a specificcontext or action, or can generate a probability distribution overstates, for example. The inference can be probabilistic—that is, thecomputation of a probability distribution over states of interest basedon a consideration of data and events. Inference can also refer totechniques employed for composing higher-level events from a set ofevents and/or data.

Such inference can result in construction of new events or actions froma set of observed events and/or stored event data, whether or not theevents are correlated in close temporal proximity, and whether theevents and data come from one or several event and data sources. Variousclassification (explicitly and/or implicitly trained) schemes and/orsystems (e.g., support vector machines, neural networks, expert systems,Bayesian belief networks, fuzzy logic, data fusion engines, etc.) can beemployed in connection with performing automatic and/or inferred actionin connection with the claimed subject matter.

A classifier can map an input attribute vector, x=(x1, x2, x3, x4, xn),to a confidence that the input belongs to a class, as byf(x)=confidence(class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to prognose or infer an action that a user desiresto be automatically performed. A support vector machine (SVM) is anexample of a classifier that can be employed. The SVM operates byfinding a hyper-surface in the space of possible inputs, where thehyper-surface attempts to split the triggering criteria from thenon-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approachesinclude, e.g., naïve Bayes, Bayesian networks, decision trees, neuralnetworks, fuzzy logic models, and probabilistic classification modelsproviding different patterns of independence can be employed.Classification as used herein also is inclusive of statisticalregression that is utilized to develop models of priority.

FIG. 1 illustrates a block diagram of an example, non-limiting system100 that facilitates utilizing machine learning (or probabilisticmodeling) to regulate environmental conditions of a vehicle tofacilitate occupant comfort in accordance with one or more embodimentsdescribed herein. Aspects of systems (e.g., non-limiting system 100 andthe like), apparatuses or processes explained in this disclosure canconstitute machine-executable component(s) embodied within machine(s),e.g., embodied in one or more computer readable mediums (or media)associated with one or more machines. Such component(s), when executedby the one or more machines, e.g., computer(s), computing device(s),virtual machine(s), etc. can cause the machine(s) to perform theoperations described.

In various embodiments, system 100 can be any type of mechanism,machine, device, facility, apparatus, and/or instrument that includes aprocessor and/or is capable of effective and/or operative communicationwith a wired and/or wireless network. Mechanisms, machines, apparatuses,devices, facilities, and/or instrumentalities that can comprisenon-limiting system 100 can include, but are not limited to, vehicles,planes, recreational vehicles (RVs), vessels, trains, passenger cabins,homes and/or mobile homes and can be implemented at least in partutilizing a variety of devices or appliances including, but not limitedto, tablet computing devices, handheld devices, server class computingmachines and/or databases, laptop computers, notebook computers,on-board vehicle computing devices or systems, desktop computers, cellphones, smart phones, consumer appliances and/or instrumentation,industrial and/or commercial devices, hand-held devices, digitalassistants, multimedia Internet enabled phones, multimedia players, andthe like.

The system 100 can include a bus 102 that can provide forinterconnection of various components of the system 100. It is to beappreciated that in other embodiments one or more system components cancommunicate wirelessly with other components, through a direct wiredconnection or integrated on a chipset. The system 100 can include one ormore sensors (e.g., sensors that detect temperature, pressure, light,image, humidity, pollution, smell, smoke, draft, moisture, air quality,particulate, accelerometers, vibration, noise, tone, weight, etc.) thatcollect information regarding environments external and/or internal to avehicle, occupants and the vehicle itself and its associated equipment.In some embodiments, context component 106 can collect and providecontextual data regarding the vehicle and/or vehicle occupants. Forexample, context information such as what activity (e.g., running,swimming, having lunch, sleeping, drinking, etc.) in which an occupantwas engaging prior to entering the vehicle can be determined orinferred. Likewise, context information regarding destination of theoccupant and/or vehicle, and/or regarding operational information aboutthe vehicle can be collected and provided to the system 100 for analysisin connection with regulating vehicle environment to facilitate occupantcomfort.

The context component 106 can, for example, obtain context informationfrom many different sources e.g., an occupant cell phone, calendar,email, GPS, appliances, third parties, the vehicle, etc. The system 100can include a processor 108 and memory 110 that can carry outcomputational and/or storage operations of the system 100 as describedherein. A communications component 112 can provide for transmitting andreceiving information (e.g., through one or more internal or externalnetworks 114 (wired or wireless networks)). A comfort model component116 can be an explicitly and/or implicitly trained machine learningcomponent trained to determine and/or infer level of occupant comfortand can determine and/or infer adjustments to environment of the vehicleto facilitate occupant comfort. A comfort controller component 120 canregulate one or more vehicle components (e.g., air conditioner, heater,humidifier, de-humidifier, lights, stereo, noise cancellationcomponents, shock absorbers, seats, pressure regulators, filters, pumps,motors, steering components, defogger, de-icer, seat warmers, seatcoolers, displays, etc.) based on output from the comfort modelcomponent 116 to facilitate achieving a vehicle environment conducive tooccupant comfort.

In an example, non-limiting implementation, a driver can enter a vehicleand the context component 106 can determine from a mobile device of theoccupant (e.g., via a BLUETOOTH® or other wired or wireless connection)that the occupant just finished a hot yoga class and/or can determine(e.g., via a wearable computing device) that the occupant has anelevated body temperature, is exhausted and in a rush to get home toclean up, dress and attend a meeting in one hour. This contextinformation can be shared with the comfort model component 116, whichcan perform a probabilistic-based analysis on the context information aswell as information from the sensor(s) 104, occupant preference andhistorical data stored in memory, state information and/or otherinformation that have relevancy to occupant comfort. The comfort modelcomponent 116, based on the analyses, can output one or morerecommendations that can be utilized by the comfort controller component120 to adjust environmental conditions of the vehicle to facilitateachieving occupant comfort. For example, the comfort model component116, based on occupant context information regarding just completing hotyoga, having elevated body temperature, being under some stress and in aslight rush, can generate an inference that the occupant will need acooler cabin temperature than normal in order to be quickly cooled downand feel comfortable. Additionally, the comfort model component 116 canactivate the in seat massage components built into the driver's seat ofthe vehicle as well as play relaxing music, adjust shock absorbers toprovide a softer ride, and adjust lighting in the cabin to effect acalming vehicle environment. The context component 106, and sensors 104can continually collect data that is analyzed by the comfort modelcomponent 116, which will generate determinations or inferencesregarding level of occupant comfort. The comfort controller component120 can continually adjust vehicle environmental conditions to maintainoccupant comfort. For example, as the occupant is starting to cool down,the comfort controller component 120 can adjust temperature by raisingtemperature slightly, reduce force of fans blowing air on occupant, slowdown or cease the seat massage, change volume of music, etc. Thus, thesystem 100 is adaptive and can employ closed or open-looped systems tofacilitate maintaining occupant comfort even as conditions of theoccupant change. It is to be appreciated that the described and claimedsubject matter contemplates and is intended to cover self-drivingvehicles.

FIG. 2 illustrates a block diagram of an example, non-limiting system200 that utilizes machine learning systems (or probabilistic modeling)to regulate environmental conditions within a vehicle to improve drivingcomfort in accordance with one or more embodiments described herein.

FIG. 2 illustrates an implementation of system 11 that includes aprofile component 202 that builds and stores in memory 110 vehicleoccupant comfort profiles. An owner of a vehicle is commonly a frequentuser of the vehicle, and the comfort model component 116 can build aspecific model for the owner as well as respective models for otherfrequent occupants (e.g., family members, close friends, pets . . . ).Upon identification of an occupant having entered the vehicle, e.g., viafacial recognition, seating pattern (e.g., body contours, weight, etc.),biometrics, voice recognition, iris recognition, cell phone, car keys,or any other suitable means for identification and authentication, theprofile component 202 can access specific profiles for each occupant ofthe vehicle that can be utilized by the comfort model component 116 togenerate determinations or inferences regarding occupant comfort leveland generating recommendations to the comfort controller component 120to adjust environment of the vehicle to achieve occupant(s) comfort. Itis to be appreciated that when multiple occupants are in the vehicle,their respective profiles may conflict in certain aspects, e.g.,temperature preference, volume preference, music preference, lightingpreference, etc.

The comfort model component can utilize the respective profiles andspecific occupant models to achieve a happy medium that achieves levelsof comfort suitable for most or all occupants. A utility-based analysiscan also be employed where the costs of taking a certain action areweighed against the benefits. For example, if a passenger prefers loudmusic but such music is a dangerous distraction for the driver who losesfocus of attention when exposed to loud music, the comfort modelcomponent 116 may determine or infer that the probability and cost ofgetting into an accident outweighs the benefit of playing such music.The comfort model component 116 can also generate recommendations to thecomfort controller to adjust environment of respective zones of thevehicle differently based on occupants within each zone. Additionally,if the comfort model component 116 determines or infers that a driver ofthe vehicle is fatigued or drowsy, the comfort model component 116 candrive the comfort controller component 120 to make adjustments to theenvironment (e.g., decrease temperature, increase stereo volume,increase brightness of display and vehicle interior, etc.) to increaselevel of alertness of the driver. Optionally, the system 100 can providea notification to the driver to pull over and rest.

FIG. 3 illustrates an alternative implementation of system 100 thatincludes a pattern recognition component 302. A set of the sensors 104can include cameras that collect image data inside and outside of thevehicle. The pattern recognition component 302 can be employed toidentify occupants, collect facial expression information that can beanalyzed by the comfort model component 116 to assess state ofoccupant(s), e.g., tired, hot, cold, sleepy, alert, sad, happy, nervous,stressed, etc. Based on such determination or inference, the comfortmodel component 116 can generate recommendations to the comfortcontroller component 120 to adjust vehicle environment. Additionally,the pattern recognition component can facilitate determining vehicledriving conditions, e.g., the window appears fogged and thus the comfortmodel component 116 can direct the comfort controller component 120 todefog the windows. The pattern recognition component 302 can detectoutside environmental activity, e.g., road conditions, traffic, weather,pedestrians, moving objects, etc. that can be utilized by the comfortmodel component 116 to instruct the vehicle to regulate operation, e.g.,slow down, brake, swerve, switch to low gear, turn on high beams, honkthe horn, etc. In another example, the pattern recognition component 302and possibly a light detection sensor 104 could determine that incomingsunlight is distractive to a driver, and automatically activate windowtinting or movement of a motorized visor to mitigate impact of sunlightor glare.

FIG. 4 illustrates an example, non-limiting, schematic representation ofa vehicle interior 400. The interior 400 includes a set of occupantseats 402 that can include sensors (e.g., weight, temperature, moisture,etc.) and environmental comfort components (e.g., seat heaters, coolers,massage components, seat adjustment components, vents, blowers, etc.)that can be regulated by the comfort model component 116 to facilitateoccupant comfort. Cameras 404 can be situated about the vehicle cabin tocollect image information. Vents/blowers 406 can be situated throughoutthe vehicle to facilitate temperature regulation as well as humidity.Additional sensors 408 can be located at various locations within andoutside of the vehicle cabin. These sensors can collect informationregarding state of the vehicle and/or occupants (e.g., temperature,light, moisture, pressure, weight, ice formation, noise, etc.). Asdiscussed above, the vehicle can be divided into respective zones (e.g.,rear right side, rear center, rear left side, driver side, frontpassenger side) and information regarding the respective zones andoccupants therein can be collected (e.g., via sensors 104, 408 andcontext component 106) and the collected information analyzed by thecomfort model component 116 to infer or determine respective occupantcomfort in the zones. Based on the inference or determination, thecomfort model component can direct the comfort controller component 120to adjust environmental conditions (e.g., seat position, seattemperature, blower intensity, zone temperature, zone moisture level,zone noise level, music volume, zone lighting, massage, window tint,visor position, heads-up display, entertainment, wireless signalstrength, video, entertainment choices, headset volume, displayfeatures, etc.) in respective zones to facilitate occupant comfort ineach zone. For example, a family enters the vehicle cabin 400 and thecontext component 106 can determine that the family is going on an 8hour car trip to a vacation destination. The sensors 104, along withpattern recognition component 302 can facilitate identifying whichfamily member is seated in a particular seat. The comfort modelcomponent 116 can utilize profiles generated by the profile component202 to generate inferences and determinations regarding suitableenvironmental conditions for each passenger in their respective zones tofacilitate comfort for each family member. Based in part on output fromthe comfort model component 116, the comfort controller component 120can adjust each zone respectively to facilitate occupant comfort, e.g.,configure seat positions based passenger preferences and context,regulate temperature and humidity in each zone, control type and volumeand display of entertainment per passenger preference. The system 100can continually monitor occupant state, context and comfort levels anddynamically adjust environmental conditions. For instance, if the rearpassengers fall asleep, the comfort controller component 120 can raisetemperature in the rear, reduce lighting, reduce volume of music,decline seats, etc. to facilitate sound sleeping comfort. Additionally,if the system 100 deems that it is time for the passengers to take abreak and walk to circulate blood throughout the body and stretch, thesystem 100 can send the driver a notification to pull over at the nextrest station for a break and to refuel the vehicle.

FIG. 5 illustrates various example, non-limiting body parameters 510(e.g., blood pressure, heart rate, pulse, skin temperature, respiratoryrates, skin humidity, blood oxygen saturation . . . ) and contextualparameters 512 (e.g., in/out car air temperature, in/out car humidity,in/out car pm2 5/TVOC, in/out car db value, in/out car light intensity .. . ) that can be utilized to assess diving car comfort index 514 andregulate car comfort facilities 516. A model can be built for inferringindividual occupant comfort degree based on his/her body parameters andcontextual parameters. The model can analyze contextual parameters thatdamage occupant(s) comfort when analyzed occupant(s) are determined orinferred to be uncomfortable. Vehicle facilities can be adjustedappropriately to improve upon the damaging contextual parameters andoccupant comfort degree change can be utilized as feedback foroptimization.

FIG. 6 illustrates that occupant state 610 and occupant context 612 canfactor occupant comfort level 614 and that such state/contextualinformation can be used by the system 100 to regulate environment of thevehicle to facilitate occupant comfort. For example, if a same occupantis running 620 prior to entering a vehicle, he/she may feel morecomfortable at a cooler temperature as compared to if the occupant justfinished swimming 624 in a cool pool. Accordingly, data collected frombody sensors can indicate an occupant's current body condition which canimpact his/her feelings in connection with driving contextualparameters. The system 100 can factor such contextual data (e.g., priorand current state and/or context) to adjust vehicle environmentalconditions to facilitate comfort.

FIGS. 7 and 8 illustrate example architectures that facilitate trainingcomfort model(s) and vehicle facility adjustment in accordance withembodiments described herein. A continuous comfort index rather thanconventional notion of simplistic comfort/un-comfort is in moreaccordance with nature of a human's feeling regarding comfort. Acontinuous comfort index allows for a better occupant experience byallowing for improving upon degree of comfort. For example, the system100 can suggest adjusting the air conditioning (AC) from 28C to 26C inorder to improve one's comfort index from 0.8 to 0.9. A conventionalcomfort/un-comfort based approach cannot accomplish such real-timeautomated refinement to facilitate occupant comfort. A continuouscomfort index allows finer granular and more cost effective control onvehicle facilities. For example, it can cost less to adjust to improvecomfort index from 0 to 0.6 than to a static 1.0. The conventionalcomfort/un-comfort based approach accomplish such fine tuning due to itsstatic nature.

With reference to FIG. 7, an architecture 700 for training a comfortmodel 710 is shown. Body and contextual sensors data 710 and driver'sactions data regarding car facilities with pre and post facility status712 are utilized to construct training samples 714. Thereafter, a build716 is performed to generate a comfort inference model 718 usingtraditional machine learning (e.g., regression). Inferences regardingdriver's comfort 720 are utilized in connection with adjustment(s) 722(e.g., determine potential driving environmental parameters that can beadjusted to improve driving comfort) and collected car comfort status724 are utilized for configuration 726 of comfort facilities (e.g.,employing feedback regarding inferred driver's current comfort 720) torealize content adjustment in connection with overall facilityadjustment 730 to achieve occupant comfort.

Referring now to FIG. 8, an architecture 800 is shown that facilitatesachieving and maintaining occupant comfort 802. A comfort inferringmodel builder 812 builds a model that infers occupant comfort within avehicle utilizing data receiving from body sensors 814 (e.g., bodytemperature, blood pressure, heart rate, glucose level, fatigue,drowsiness, alertness . . . ), driving contextual parameter collector816 (e.g., prior or current activities . . . ), and a comfort facilitiesstatus collector 818 (e.g., state of comfort facilities). A drivercomfort degree analyzer 815 determines or infers degree of drivercomfort utilizing information received from the body sensors 814 and thecomfort inferring model builder 812. A comfort facilities adjustmentbuilder 820 receives data from the comfort facilities status collector818, the driving contextual parameter collector 816 and builds a modelfor adjusting comfort facilities. A comfort facilities adjustmentanalyzer 822 analyzes comfort facilities utilizing information from thedriver comfort degree analyzer 815, the driving contextual parametercollector 816 the comfort facilities status collector 818 and thecomfort facilities adjustment builder 820 to generate adjustments to bemade by a comfort facilities controller 824 that regulates vehicleenvironment (e.g., temperatures, sound, humidity, air circulation,vibration, massage, lighting, etc.) in connection with facilitatingoccupant comfort 802.

FIG. 9. illustrates an architecture 900 for inferring occupant comfortin connection with a driver's adjustment on car facilities as a“un-comfort” indicator, the number of actions, and gap of facilitiesstatus before and after the actions are used for comfort indexcalculation. For example, one can adjust air conditioning (AC) from 28Cto 26C if an occupant feels a little hot, and will adjust the AC to 24Cif the occupant is very hot. For example, referring to Table 1 below,exemplary dependent variables (e.g., features) are listed in connectionwith driving comfort index (0-1).

TABLE 1 NAME CATEGORY EXAMPLE Heartbeat Body sensor data 75 times Skintemperature Body sensor data 35.8 C. Respiratory rates Body sensor data16 times Skin humidity Body sensor data 48% (RH) Blood oxygen saturationBody sensor data 99% In-car air temperature Contextual sensor data 29 C.In-car humidity Contextual sensor data 55% (RH) In-car pm2.5 Contextualsensor data 87   In-car tvoc Contextual sensor data 0.6 In-car db valueContextual sensor data 32 db In-car light intensity Contextual sensordata 100 ux

Referring back to FIG. 9, an architecture 900 for training a comfortmodel is shown. Body and contextual sensors data 912 and driver'sactions on car facilities with pre and post facility status 914 areutilized to construct training samples 916. Thereafter, a build 918 isperformed to generate a comfort inference model 920 using traditionalmachine learning (e.g., regression). Data from the sensors 912 areutilized to continually update the comfort inference model 920.

FIG. 10 illustrates exemplary, non-limiting, construction of trainingsamples 1000 in connection with comfort model training/building inaccordance with embodiments described herein. At 1010, combined body andcontextual sensors data are sampled at a same time point to form afeature vector (e.g., 75, 35.8, 16, 48, 99, 29, 55, 87, 0.6, 32, 100).At 1020, driver's actions are correlated to the vector is the actionsoccur within the vector's forward time window 1016. At 1030, the degreeto which all correlated actions change status of car facilities areevaluated. At 1040, the current vector and its similar vectors in abackward time window are marked as “un-comfort” (e.g., positivesamples). At 1050, other un-marked backward vectors are marked as“comfort” (e.g., negative samples). At 1060, a comfort index iscalculated for the samples. It is to be noted that for positive samplesthe comfort index=1. For negative samples, the comfort index iscalculated based on associated facilities adjustment degree.

Referring to Table 2 below and FIG. 11, an example calculation ofcomfort index for negative sample(s) in accordance with one or moreembodiments described herein is presented. At 1100, car facilitypre/post adjustment status vectors are built (e.g., pre vector: (28,50%, 0.8); post vector: (26, 0, 0.5). At 1102, status vectors arenormalized based on adjustable range (e.g., pre vector: (0.8, 0.5, 0.8);post vector: (0.6, 0, 0.5) where 0.6=(26−20)/(30−20). At 1104, distancebetween pre/post vectors is calculated (e.g., Euclidean distance (pre-v,post-v)=0.62). At 1106, comfort index is calculated (e.g., comfortindex=1−distance=0.38.

TABLE 2 Pre-adjustment Post-adjustment Car Facility Status StatusAdjustable Range AC 28 C. 26 C. 20-30 C. Window 50% open 0% open 0-100%open Radio sound 0.8 Max 0.5 Max 0-1 Max volume

FIG. 12 illustrates a flow diagram of an example, non-limitingcomputer-implemented method 1200 for determination of damaging drivingenvironmental parameters that can negatively impact occupant comfort. At1210, body sensor data is obtained of a current “un-comfort” vector. At1220, “comfort” vectors are clustered by respective body sensor data. At1230, closest “comfort” clusters by body sensor data are found. At 1240,contextual sensor data of a current “un-comfort” vector is obtained. At1250, for each cluster representative contextual parameters arecalculated. At 1260, a comparison is made with the cluster'srepresentative contextual parameters to identify those parameters worsethan a benchmark.

FIG. 13 illustrates a flow diagram of an example, non-limitingcomputer-implemented method 1300 that vehicle occupant comfort inaccordance with one or more embodiments described herein. At 1302,ambient conditions associated with exterior and interior conditions of avehicle are sensed, including but not limited to sensed informationassociated with the vehicle and/or occupants of the vehicle. Forexample, sensed information may include one or more of ambienttemperature, light levels, odors, smoke, pollutants, allergens,pressure, moisture, sound levels, noise levels, weather conditions, roadconditions, vehicle speed, and direction.

At 1304, occupant context can be determined or inferred (e.g., via acontext component 106). For example, occupant context can include prioractivities, current activities, stress level, emotional state,destination, upcoming events, attire, alertness, fatigue level,sleepiness level, focus of attention, mood, health state, etc. Extrinsicor intrinsic information regarding an occupant can be collected from avariety of sources (e.g., cell phones, GPS, email, text messages,calendars, appointments, wearable computing devices, cloud-basedservices, 3rd parties, the vehicle, etc.).

At 1306 the sensed information and occupant context information areanalyzed using a trained machine learning system (e.g., comfort modelcomponent 116).

At 1308 a determination is made regarding whether or not the occupant iscomfortable. For example, a comfort index can be utilized to assesswhether the occupant is comfortable or if comfort can be improved upon.If Yes, the process returns to 1302. If No, an environmental regulationsystem (e.g., comfort controller component 120) is utilized to adjustenvironmental conditions until the occupant is deemed or inferred to becomfortable.

For simplicity of explanation, the computer-implemented methodologiesare depicted and described as a series of acts. It is to be understoodand appreciated that the subject innovation is not limited by the actsillustrated and/or by the order of acts, for example acts can occur invarious orders and/or concurrently, and with other acts not presentedand described herein. Furthermore, not all illustrated acts can berequired to implement the computer-implemented methodologies inaccordance with the disclosed subject matter. In addition, those skilledin the art will understand and appreciate that the computer-implementedmethodologies could alternatively be represented as a series ofinterrelated states via a state diagram or events. Additionally, itshould be further appreciated that the computer-implementedmethodologies disclosed hereinafter and throughout this specificationare capable of being stored on an article of manufacture to facilitatetransporting and transferring such computer-implemented methodologies tocomputers. The term article of manufacture, as used herein, is intendedto encompass a computer program accessible from any computer-readabledevice or storage media.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 14 as well as the following discussion are intendedto provide a general description of a suitable environment in which thevarious aspects of the disclosed subject matter can be implemented. Oneor more of the embodiments described herein and in connection with oneor more of FIGS. 1-13 can be implemented in connection with theoperating environment or portion thereof in connection with FIG. 14. Forexample, A computer-implemented method that comprises: sensing, usingone or more sensors, ambient conditions associated with one or moreconditions of a vehicle; determining, by a device operatively coupled toa processor, context of an occupant of the vehicle; analyzing, by thedevice, one or more ambient conditions and the context; and adjustingone or more environmental conditions of a passenger compartment of thevehicle based on the analyzing the one or more ambient conditions andthe context can be implemented utilizing at least certain aspects andfeatures of the operating environment illustrated and described inconnection with FIG. 14.

FIG. 14 illustrates a block diagram of an example, non-limitingoperating environment in which one or more embodiments described hereincan be facilitated. Repetitive description of like elements employed inother embodiments described herein is omitted for sake of brevity. Withreference to FIG. 14, a suitable operating environment 1401 forimplementing various aspects of this disclosure can also include acomputer 1412. The computer 1412 can also include a processing unit1414, a system memory 1416, and a system bus 1418. The system bus 1418couples system components including, but not limited to, the systemmemory 1416 to the processing unit 1414. The processing unit 1414 can beany of various available processors. Dual microprocessors and othermultiprocessor architectures also can be employed as the processing unit1414. The system bus 1418 can be any of several types of busstructure(s) including the memory bus or memory controller, a peripheralbus or external bus, and/or a local bus using any variety of availablebus architectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI). The system memory 1416 can alsoinclude volatile memory 1420 and nonvolatile memory 1422. The basicinput/output system (BIOS), containing the basic routines to transferinformation between elements within the computer 1412, such as duringstart-up, is stored in nonvolatile memory 1422. By way of illustration,and not limitation, nonvolatile memory 1422 can include read only memory(ROM), programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), flash memory, ornonvolatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM).Volatile memory 1420 can also include random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as static RAM (SRAM),dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM(DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), directRambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambusdynamic RAM.

Computer 1412 can also include removable/non-removable,volatile/non-volatile computer storage media. FIG. 14 illustrates, forexample, a disk storage 1424. Disk storage 1424 can also include, but isnot limited to, devices like a magnetic disk drive, floppy disk drive,tape drive, Jaz drive, Zip drive, LS-100 drive, flash memory card, ormemory stick. The disk storage 1424 also can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage 1424 to the system bus 1418, a removableor non-removable interface is typically used, such as interface 1426.FIG. 14 also depicts software that acts as an intermediary between usersand the basic computer resources described in the suitable operatingenvironment 1401. Such software can also include, for example, anoperating system 1428. Operating system 1428, which can be stored ondisk storage 1424, acts to control and allocate resources of thecomputer 1412. System applications 1430 take advantage of the managementof resources by operating system 1428 through program modules 1432 andprogram data 1434, e.g., stored either in system memory 1416 or on diskstorage 1424. It is to be appreciated that this disclosure can beimplemented with various operating systems or combinations of operatingsystems. A user enters commands or information into the computer 1412through input device(s) 1436. Input devices 1436 include, but are notlimited to, a pointing device such as a mouse, trackball, stylus, touchpad, keyboard, microphone, joystick, game pad, satellite dish, scanner,TV tuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1414through the system bus 1418 via interface port(s) 1438. Interfaceport(s) 1438 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1440 usesome of the same type of ports as input device(s) 1436. Thus, forexample, a USB port can be used to provide input to computer 1412, andto output information from computer 1412 to an output device 1440.Output adapter 1442 is provided to illustrate that there are some outputdevices 1440 like monitors, speakers, and printers, among other outputdevices 1440, which require special adapters. The output adapters 1442include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1440and the system bus 1418. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asremote computer(s) 1444.

Computer 1412 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1444. The remote computer(s) 1444 can be a computer, a server, a router,a network PC, a workstation, a microprocessor based appliance, a peerdevice or other common network node and the like, and typically can alsoinclude many or all of the elements described relative to computer 1412.For purposes of brevity, only a memory storage device 1446 isillustrated with remote computer(s) 1444. Remote computer(s) 1444 islogically connected to computer 1412 through a network interface 1448and then physically connected via communication connection 1450. Networkinterface 1448 encompasses wire and/or wireless communication networkssuch as local-area networks (LAN), wide-area networks (WAN), cellularnetworks, etc. LAN technologies include Fiber Distributed Data Interface(FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ringand the like. WAN technologies include, but are not limited to,point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL). Communicationconnection(s) 1450 refers to the hardware/software employed to connectthe network interface 1448 to the system bus 1418. While communicationconnection 1450 is shown for illustrative clarity inside computer 1412,it can also be external to computer 1412. The hardware/software forconnection to the network interface 1448 can also include, for exemplarypurposes only, internal and external technologies such as, modemsincluding regular telephone grade modems, cable modems and DSL modems,ISDN adapters, and Ethernet cards.

Embodiments of the present invention may be a system, a method, anapparatus and/or a computer program product at any possible technicaldetail level of integration. The computer program product can include acomputer readable storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outaspects of the present invention. The computer readable storage mediumcan be a tangible device that can retain and store instructions for useby an instruction execution device. The computer readable storage mediumcan be, for example, but is not limited to, an electronic storagedevice, a magnetic storage device, an optical storage device, anelectromagnetic storage device, a semiconductor storage device, or anysuitable combination of the foregoing. A non-exhaustive list of morespecific examples of the computer readable storage medium can alsoinclude the following: a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a static randomaccess memory (SRAM), a portable compact disc read-only memory (CD-ROM),a digital versatile disk (DVD), a memory stick, a floppy disk, amechanically encoded device such as punch-cards or raised structures ina groove having instructions recorded thereon, and any suitablecombination of the foregoing. A computer readable storage medium, asused herein, is not to be construed as being transitory signals per se,such as radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device. Computer readable programinstructions for carrying out operations of various aspects of thepresent invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions can executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer can be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection can be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) can execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to customize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions. These computer readable programinstructions can be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks. These computer readable program instructions can also be storedin a computer readable storage medium that can direct a computer, aprogrammable data processing apparatus, and/or other devices to functionin a particular manner, such that the computer readable storage mediumhaving instructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks. Thecomputer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational acts to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successioncan, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this disclosure also can or can be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive computer-implemented methods can be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as computers, hand-held computing devices (e.g., PDA,phone), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of this disclosure can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution and a component canbe localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, wherein the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration. For the avoidance of doubt, the subject matterdisclosed herein is not limited by such examples. In addition, anyaspect or design described herein as an “example” and/or “exemplary” isnot necessarily to be construed as preferred or advantageous over otheraspects or designs, nor is it meant to preclude equivalent exemplarystructures and techniques known to those of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and number-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor can also beimplemented as a combination of computing processing units. In thisdisclosure, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory can include RAM, which canact as external cache memory, for example. By way of illustration andnot limitation, RAM is available in many forms (such as in the examplesprovided above). Additionally, the disclosed memory components ofsystems or computer-implemented methods herein are intended to include,without being limited to including, these and any other suitable typesof memory.

What has been described above include mere examples of systems, computerprogram products and computer-implemented methods. It is, of course, notpossible to describe every conceivable combination thereof for purposesof describing this disclosure, but one of ordinary skill in the art canrecognize that many further combinations and permutations are possible.Furthermore, to the extent that the terms “includes,” “has,”“possesses,” and the like are used in the detailed description, claims,appendices and drawings such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transition word in a claim. The descriptions of thevarious embodiments have been presented for purposes of illustration,but are not intended to be exhaustive or limited to the embodimentsdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. The terminology used herein was chosen tobest explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A computer-implemented method, comprising: training, using a processor operatively coupled to a memory, a comfort model corresponding to an occupant of a vehicle, by applying a machine learning approach to training samples, wherein a training sample includes: prior ambient conditions associated with the vehicle and prior body parameters associated with the occupant; a pre-adjustment status and a post-adjustment status of comfort facilities of the vehicle, corresponding to the prior ambient conditions and the prior body parameters; and a prior continuous comfort index based on a Euclidean distance between the pre-adjustment and post-adjustment statuses; sensing, via a plurality of sensors, current ambient conditions associated with the vehicle and current body parameters associated with the occupant; determining, using the processor, a context of the occupant; inferring, using the processor and the comfort model, a current continuous comfort index indicating a current comfort level of the occupant, based on the current ambient conditions, the current body parameters, and the context; determining, using the processor, one or more of the current ambient conditions to be adjusted to improve the current comfort level of the occupant, by: clustering, according to prior body parameters, the training samples that have a prior continuous comfort index better than the current continuous comfort index; identifying a cluster having prior body parameters closest to the current body parameters; calculating representative ambient conditions based on the prior ambient conditions of the identified cluster; and identifying, as the determined one or more of the current ambient conditions, one or more of the current ambient conditions that are worse, by a predetermined benchmark, than the representative ambient conditions; and adjusting environmental conditions of a passenger compartment of the vehicle based on the determined one or more of the current ambient conditions.
 2. The method of claim 1, further comprising using the plurality of sensors to sense information selected from the group consisting of body temperature of the occupant and an amount of clothing worn by the occupant.
 3. The method of claim 1, further comprising determining the context using information selected from the group consisting of an action of the occupant, a destination for the occupant, and a trip length for the occupant.
 4. The method of claim 1, further comprising further comprising using the comfort model to determine an environmental condition for at least one of one or more occupants of the vehicle.
 5. The method of claim 1, further comprising using the processor to determine one or more comfort profiles for high frequency occupants of the vehicle.
 6. The method of claim 1, further comprising using the processor to identify, via facial recognition, the occupant of the vehicle.
 7. The method of claim 1, further comprising using the processor to infer, via pattern recognition, occupant comfort from one or more facial expressions.
 8. A method, comprising: training, using a processor operatively coupled to memory, a comfort model corresponding to an occupant of a vehicle, by applying a machine learning approach to training samples, wherein a training sample includes: prior ambient conditions associated with the vehicle and prior body parameters associated with the occupant; a pre-adjustment status and a post-adjustment status of comfort facilities of the vehicle, corresponding to the prior ambient conditions and the prior body parameters; and a prior continuous comfort index based on a Euclidean distance between the pre-adjustment and post-adjustment statuses; sensing, via one or more sensors, current ambient conditions associated with the vehicle and current body parameters associated with the occupant; inferring, by the comfort model, a current continuous comfort index indicating a current comfort level of the occupant, based on the current ambient conditions and the current body parameters; determining, using the processor, one or more of the current ambient conditions to be adjusted to improve the current comfort level of the occupant, by: clustering, according to prior body parameters, the training samples that have a prior continuous comfort index better than the current continuous comfort index; identifying a cluster having prior body parameters closest to the current body parameters; calculating representative ambient conditions based on the prior ambient conditions of the identified cluster; and identifying, as the determined one or more of the current ambient conditions, one or more of the current ambient conditions that are worse, by a predetermined benchmark, than the representative ambient conditions; and adjusting, using the processor, one or more of the comfort facilities of the vehicle corresponding to the determined one or more of the current ambient conditions.
 9. The method of claim 8, wherein the machine learning approach is a regression analysis.
 10. The method of claim 8, further comprising inferring, using the processor, a context of the occupant, including at least a recent activity of the occupant, and wherein the current continuous comfort index is based in part on the context. 